The　increasing　complexity　and　dynamics　of　the　stock　trading　market　are　major　challenges　for　the　financial　industry　and　are　primary　dilemmas　for　all　countries　nowadays.　In　addition,　the　stock　trading　market　has　a　considerable　impact　on　the　global　economy,　and　its　importance　is　self-evident.　To　cope　with　the　complexity　and　dynamics　of　a　stock　trading　market,　this　paper　applies　complex　network　theory　and　model　to　explore　the　topology　of　the　global　stock　trading　network.　First,　this　paper　collects　stock　trading　data　from　74　countries　from　1999　to　2020.　It　converts　the　collected　stock　trading　data　of　these　countries　into　a　complex　network　using　a　type　of　algorithm　based　on　the　time　series　visibility　graph　(VG)　algorithm.　Then,　the　data　are　analyzed　by　a　complex　network　model,　and　six　analytical　metrics　are　obtained.　Finally,　the　six　metrics　are　analyzed　by　the　entropy　weight　method　to　identify　the　key　nodes　in　the　network　and　to　obtain　the　ranking　of　each　country＇s　stock　trading　data.　This　paper　is　an　effective　application　of　complex　network　and　entropy　weight　method　in　stock　trend　analysis,　which　mainly　includes　two　contributions.　First,　the　VG　algorithm　provides　a　novel　research　perspective　for　modeling　the　global　stock　trading　trend.　Second,　key　nodes　in　the　network　are　analyzed　and　identified　based　on　the　entropy　weight　method,　and　the　ranking　of　key　nodes　in　the　stock　trading　network　is　obtained,　which　provides　a　new　method　for　further　research　on　the　stock　trading　trend,　investment　portfolio,　and　stock　return　forecasting.